Chair of Molecular Technology

The chair was established in 1992 and was initially called as the chair of theoretical chemistry. In 2004, it was renamed as the chair of molecular technology, due to changes in research interests. Since the very beginning, the chair has been headed by professor Mati Karelson.

Molecular technology is an interdisciplinary area based on fundamental sciences such as chemistry, physics, and biology. The prerequisite for understanding and application of macromolecular systems is knowledge about the properties of individual molecules and their interactions. Therefore modern chemical and biotechnology, nanotechnology, design of new medicines and high technology materials, and other fields need data about the chemical structures and properties. Computer technology and science has gone through a rapid progress in the last decades of 20th century, which has opened completely new ways for understanding and modelling of these systems. Molecular technology is offering a symbiosis between experimental and computer design and enables to create new and more efficient solutions.

Teaching

The main focus is teaching at the master and PhD level. The chair coordinates the curriculum of molecular technology, which is interdisciplinary and educates new researchers who are able to work at the borderline and frontiers of fundamental fields of chemistry, physics, and biology, such as quantum and molecular electronics, development of new energy sources and materials, biomedicine, molecular biology, genetic engineering, environmental monitoring, and others. The chair is productive in providing high education, which is also characterised by successfully defending 15 PhD and 13 MSc theses since the establishment of the chair.

Main research and development topics

Research and practical application of quantum chemical methods for describing molecular systems in condensed environments (liquids, solutions, and polymers).

Design and implementation of modern technology for the development of quantitative structure-activity/property relationship models. Application of models for the prediction of properties or activities of chemicals or drug candidates.

Application of artificial intelligence methods (artificial neural networks, data mining, etc.) in chemistry and related fields to discover new knowledge and new structures with desired properties.

Molecular design on biotechnological and macromolecular systems: molecular docking; development and analysis of virtual libraries of chemical compounds; high throughput screening of proteins and chemicals (including the analysis of ADME/Tox profiles).